from IPython.display import Image, display
import os

# Visualiza 3 imágenes de entrenamiento
image_dir = "dataset_final_yolo_v8/images/train"
sample_images = os.listdir(image_dir)[:3]
for img in sample_images:
    display(Image(filename=os.path.join(image_dir, img)))

from pathlib import Path
import os

# Ruta base del dataset
dataset_path = Path("/home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data")
assert dataset_path.exists(), f"❌ Ruta no encontrada: {dataset_path}"

# Crear un archivo dataset.yaml reproducible
yaml_content = """
path: /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo
train: obj_train_data
val: obj_train_data
test: obj_train_data

names:
  0: iguana
  1: lobo_marino
  2: pelícano
  3: pingüino
  4: tortuga
"""

with open("dataset.yaml", "w") as f:
    f.write(yaml_content.strip())

print("✅ Archivo dataset.yaml generado correctamente.")

✅ Archivo dataset.yaml generado correctamente.

import os
os.remove("yolov8n.pt")

import torch
print(torch.__version__)  # ✅ Debe decir 2.1.2

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.6 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 198, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 88, in _run_code
    exec(code, run_globals)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel_launcher.py", line 18, in <module>
    app.launch_new_instance()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/traitlets/config/application.py", line 1075, in launch_instance
    app.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelapp.py", line 739, in start
    self.io_loop.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/tornado/platform/asyncio.py", line 211, in start
    self.asyncio_loop.run_forever()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 608, in run_forever
    self._run_once()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 1936, in _run_once
    handle._run()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/events.py", line 84, in _run
    self._context.run(self._callback, *self._args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 516, in dispatch_queue
    await self.process_one()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 505, in process_one
    await dispatch(*args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 397, in dispatch_shell
    await result
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 368, in execute_request
    await super().execute_request(stream, ident, parent)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 752, in execute_request
    reply_content = await reply_content
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 455, in do_execute
    res = shell.run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/zmqshell.py", line 577, in run_cell
    return super().run_cell(*args, **kwargs)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3116, in run_cell
    result = self._run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3171, in _run_cell
    result = runner(coro)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner
    coro.send(None)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3394, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3639, in run_ast_nodes
    if await self.run_code(code, result, async_=asy):
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3699, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_110141/4193278548.py", line 1, in <module>
    import torch
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/__init__.py", line 1382, in <module>
    from .functional import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/functional.py", line 7, in <module>
    import torch.nn.functional as F
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module>
    from .modules import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module>
    from .transformer import TransformerEncoder, TransformerDecoder, \
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module>
    device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.)
  device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),

2.1.2+cu121

from ultralytics import YOLO

# Carga directa sin errores
model = YOLO("yolov8n.pt")

!pip uninstall numpy -y
!pip install numpy==1.24.4

Found existing installation: numpy 2.2.6
Uninstalling numpy-2.2.6:
  Successfully uninstalled numpy-2.2.6
Collecting numpy==1.24.4
  Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)
Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 53.5 MB/s eta 0:00:00MB/s eta 0:00:01
Installing collected packages: numpy
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scipy 1.16.1 requires numpy<2.6,>=1.25.2, but you have numpy 1.24.4 which is incompatible.
contourpy 1.3.3 requires numpy>=1.25, but you have numpy 1.24.4 which is incompatible.
opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= "3.9", but you have numpy 1.24.4 which is incompatible.
Successfully installed numpy-1.24.4

import numpy as np
import scipy
print("NumPy:", np.__version__)
print("SciPy:", scipy.__version__)

NumPy: 1.24.4
SciPy: 1.11.4

# Entrenar el modelo sobre el dataset personalizado
from ultralytics import YOLO

model = YOLO("yolov8n.pt")  # Modelo base (nano)

results = model.train(
    data="dataset.yaml",        # Tu archivo .yaml con clases y rutas
    epochs=100,                 # Número de épocas
    imgsz=640,                  # Tamaño de imagen (puedes aumentar a 800 o 960 si tienes GPU potente)
    batch=8,                    # Tamaño de lote
    name="galapagos_yolov8n",   # Nombre del experimento
    project="runs/train",       # Carpeta de resultados
    optimizer="Adam",           # Optimizador (puede cambiarse por SGD)
    device=0                    # GPU 0 (usa 'cpu' si no tienes GPU)
)

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=dataset.yaml, epochs=100, time=None, patience=50, batch=8, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=runs/train, name=galapagos_yolov8n10, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov8n10
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    752287  ultralytics.nn.modules.head.Detect           [5, [64, 128, 256]]           
Model summary: 225 layers, 3011823 parameters, 3011807 gradients, 8.2 GFLOPs

/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")

Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov8n10/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/100      1.24G      1.736      3.918      1.886          2        640: 100%|██████████| 19/19 [00:07<00:00,  2.49it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 10/10 [00:02<00:00,  4.44it/s]

results = model.train(
    data="dataset.yaml",
    epochs=200,            # Más épocas
    imgsz=800,             # Imágenes más grandes
    batch=8,
    name="galapagos_yolov8n_aug",
    mosaic=1.0,            # Activar mosaic augmentation
    hsv_h=0.015, hsv_s=0.7, hsv_v=0.4,  # Perturbaciones de color
    flipud=0.5, fliplr=0.5,  # Volteos horizontales y verticales
    degrees=10, translate=0.1, scale=0.5, shear=2.0,
    device=0
)

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=dataset.yaml, epochs=200, time=None, patience=50, batch=8, imgsz=800, save=True, save_period=-1, cache=False, device=0, workers=8, project=runs/train, name=galapagos_yolov8n_aug, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=10, translate=0.1, scale=0.5, shear=2.0, perspective=0.0, flipud=0.5, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov8n_aug

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    752287  ultralytics.nn.modules.head.Detect           [5, [64, 128, 256]]           
Model summary: 225 layers, 3011823 parameters, 3011807 gradients, 8.2 GFLOPs

Transferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov8n_aug/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
200 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/200      1.86G       2.23      3.242      2.503          4        800: 100%|██████████| 19/19 [00:11<00:00,  1.66it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 10/10 [00:03<00:00,  2.80it/s]

                   all        147        198      0.336      0.133      0.103     0.0442

from ultralytics import YOLO

# Cargar el modelo YOLOv8-Large preentrenado
model = YOLO("yolov8l.pt")  # También puedes probar yolov8x.pt si tienes más GPU

# Entrenamiento con optimizaciones
results = model.train(
    data="dataset.yaml",          # Tu dataset personalizado
    epochs=300,                   # Más épocas
    imgsz=960,                    # Mayor resolución de entrada
    batch=4,                      # Ajustado para no saturar GPU con modelo grande
    name="galapagos_yolov8l",     # Nombre del experimento
    project="runs/train",         # Carpeta de salida
    optimizer="Adam",             # Optimizador más estable
    device=0,                     # GPU 0 (o 'cpu' si no hay CUDA)
    patience=30,                  # Early stopping
    verbose=True,                 # Muestra progreso
    workers=4                     # Paralelismo en carga de datos
)

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov8l.pt, data=dataset.yaml, epochs=300, time=None, patience=30, batch=4, imgsz=960, save=True, save_period=-1, cache=False, device=0, workers=4, project=runs/train, name=galapagos_yolov8l2, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov8l2
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 
  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           
  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           
  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              
  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           
  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              
  8                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           
  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]

/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")

 12                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 
 16                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 
 19                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                
 22        [15, 18, 21]  1   5586655  ultralytics.nn.modules.head.Detect           [5, [256, 512, 512]]          
Model summary: 365 layers, 43633695 parameters, 43633679 gradients, 165.4 GFLOPs

Transferred 589/595 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov8l2/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005), 103 bias(decay=0.0)
300 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

  0%|          | 0/37 [00:00<?, ?it/s]

Entrenamiento de YOLOv8 con Transfer Learning - Especies Galápagos 🦎🐧¶